OpenRaiser/Intern-Atlas huggingface dataset:
A Nested Watermark for Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

The rapid advancement of large language models (LLMs) has raised concerns regarding their potential misuse, particularly in generating fake news and misinformation. To address these risks, watermarking techniques for autoregressive language models have emerged as a promising means for detecting LLM-generated text. Existing methods typically embed a watermark by increasing the probabilities of tokens within a group selected according to a single secret key. However, this approach suffers from a critical limitation: if the key is leaked, it becomes impossible to trace the text's provenance or attribute authorship. To overcome this vulnerability, we propose a novel nested watermarking scheme that embeds two distinct watermarks into the generated text using two independent keys. This design enables reliable authorship identification even in the event that one key is compromised. Experimental results demonstrate that our method achieves high detection accuracy for both watermarks while maintaining the fluency and overall quality of the generated text.
Origins of Creativity in Attention-Based Diffusion Models
Venue: arXiv cs.LG 2025
Year: 2025

As diffusion models have become the tool of choice for image generation and as the quality of the images continues to improve, the question of how `creativity' originates in diffusion has become increasingly important. The score matching perspective on diffusion has proven particularly fruitful for understanding how and why diffusion models generate images that remain plausible while differing significantly from their training images. In particular, as explained in (Kamb \& Ganguli, 2024) and others, e.g., (Ambrogioni, 2023), theory suggests that if our score matching were optimal, we would only be able to recover training samples through our diffusion process. However, as shown by Kamb \& Ganguli, (2024), in diffusion models where the score is parametrized by a simple CNN, the inductive biases of the CNN itself (translation equivariance and locality) allow the model to generate samples that globally do not match any training samples, but are rather patch-wise `mosaics'. Notably, however, this theory does not extend to describe the role of self-attention in this process. In this work, we take a preliminary step in this direction to extend this theory to the case of diffusion models whose score is parametrized by a CNN with a final self-attention layer. We show that our theory suggests that self-attention will induce a globally image-consistent arrangement of local features beyond the patch-level in generated samples, and we verify this behavior empirically on a carefully crafted dataset.
RadarSeq: A Temporal Vision Framework for User Churn Prediction via Radar Chart Sequences
Venue: arXiv cs.CV 2025
Year: 2025

Predicting user churn in non-subscription gig platforms, where disengagement is implicit, poses unique challenges due to the absence of explicit labels and the dynamic nature of user behavior. Existing methods often rely on aggregated snapshots or static visual representations, which obscure temporal cues critical for early detection. In this work, we propose a temporally-aware computer vision framework that models user behavioral patterns as a sequence of radar chart images, each encoding day-level behavioral features. By integrating a pretrained CNN encoder with a bidirectional LSTM, our architecture captures both spatial and temporal patterns underlying churn behavior. Extensive experiments on a large real-world dataset demonstrate that our method outperforms classical models and ViT-based radar chart baselines, yielding gains of 17.7 in F1 score, 29.4 in precision, and 16.1 in AUC, along with improved interpretability. The framework's modular design, explainability tools, and efficient deployment characteristics make it suitable for large-scale churn modeling in dynamic gig-economy platforms.
Reflective VLM Planning for Dual-Arm Desktop Cleaning: Bridging Open-Vocabulary Perception and Precise Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Desktop cleaning demands open-vocabulary recognition and precise manipulation for heterogeneous debris. We propose a hierarchical framework integrating reflective Vision-Language Model (VLM) planning with dual-arm execution via structured scene representation. Grounded-SAM2 facilitates open-vocabulary detection, while a memory-augmented VLM generates, critiques, and revises manipulation sequences. These sequences are converted into parametric trajectories for five primitives executed by coordinated Franka arms. Evaluated in simulated scenarios, our system achieving 87.2% task completion, a 28.8% improvement over static VLM and 36.2% over single-arm baselines. Structured memory integration proves crucial for robust, generalizable manipulation while maintaining real-time control performance.
AutomataGPT: Forecasting and Ruleset Inference for Two-Dimensional Cellular Automata
Venue: arXiv cs.LG 2025
Year: 2025

Cellular automata (CA) provide a minimal formalism for investigating how simple local interactions generate rich spatiotemporal behavior in domains as diverse as traffic flow, ecology, tissue morphogenesis and crystal growth. However, automatically discovering the local update rules for a given phenomenon and using them for quantitative prediction remains challenging. Here we present AutomataGPT, a decoder-only transformer pretrained on around 1 million simulated trajectories that span 100 distinct two-dimensional binary deterministic CA rules on toroidal grids. When evaluated on previously unseen rules drawn from the same CA family, AutomataGPT attains 98.5% perfect one-step forecasts and reconstructs the governing update rule with up to 96% functional (application) accuracy and 82% exact rule-matrix match. These results demonstrate that large-scale pretraining over wider regions of rule space yields substantial generalization in both the forward (state forecasting) and inverse (rule inference) problems, without hand-crafted priors. By showing that transformer models can faithfully infer and execute CA dynamics from data alone, our work lays the groundwork for abstracting real-world dynamical phenomena into data-efficient CA surrogates, opening avenues in biology, tissue engineering, physics and AI-driven scientific discovery.
PPMI: Privacy-Preserving LLM Interaction with Socratic Chain-of-Thought Reasoning and Homomorphically Encrypted Vector Databases
Venue: arXiv cs.CR 2025
Year: 2025

Large language models (LLMs) are increasingly used as personal agents, accessing sensitive user data such as calendars, emails, and medical records. Users currently face a trade-off: They can send private records, many of which are stored in remote databases, to powerful but untrusted LLM providers, increasing their exposure risk. Alternatively, they can run less powerful models locally on trusted devices. We bridge this gap. Our Socratic Chain-of-Thought Reasoning first sends a generic, non-private user query to a powerful, untrusted LLM, which generates a Chain-of-Thought (CoT) prompt and detailed sub-queries without accessing user data. Next, we embed these sub-queries and perform encrypted sub-second semantic search using our Homomorphically Encrypted Vector Database across one million entries of a single user's private data. This represents a realistic scale of personal documents, emails, and records accumulated over years of digital activity. Finally, we feed the CoT prompt and the decrypted records to a local language model and generate the final response. On the LoCoMo long-context QA benchmark, our hybrid framework, combining GPT-4o with a local Llama-3.2-1B model, outperforms using GPT-4o alone by up to 7.1 percentage points. This demonstrates a first step toward systems where tasks are decomposed and split between untrusted strong LLMs and weak local ones, preserving user privacy.
PBFT-Backed Semantic Voting for Multi-Agent Memory Pruning
Venue: arXiv cs.DC 2025
Year: 2025

The proliferation of multi-agent systems (MAS) in complex, dynamic environments necessitates robust and efficient mechanisms for managing shared knowledge. A critical challenge is ensuring that distributed memories remain synchronized, relevant, and free from the accumulation of outdated or inconsequential data - a process analogous to biological forgetting. This paper introduces the Co-Forgetting Protocol, a novel, comprehensive framework designed to address this challenge by enabling synchronized memory pruning in MAS. The protocol integrates three key components: (1) context-aware semantic voting, where agents utilize a lightweight DistilBERT model to assess the relevance of memory items based on their content and the current operational context; (2) multi-scale temporal decay functions, which assign diminishing importance to memories based on their age and access frequency across different time horizons; and (3) a Practical Byzantine Fault Tolerance (PBFT)-based consensus mechanism, ensuring that decisions to retain or discard memory items are agreed upon by a qualified and fault-tolerant majority of agents, even in the presence of up to f Byzantine (malicious or faulty) agents in a system of N greater than or equal to 3f+1 agents. The protocol leverages gRPC for efficient inter-agent communication and Pinecone for scalable vector embedding storage and similarity search, with SQLite managing metadata. Experimental evaluations in a simulated MAS environment with four agents demonstrate the protocol's efficacy, achieving a 52% reduction in memory footprint over 500 epochs, 88% voting accuracy in forgetting decisions against human-annotated benchmarks, a 92% PBFT consensus success rate under simulated Byzantine conditions, and an 82% cache hit rate for memory access.
AI is the Strategy: From Agentic AI to Autonomous Business Models onto Strategy in the Age of AI
Venue: arXiv cs.CY 2025
Year: 2025

This article develops the concept of Autonomous Business Models (ABMs) as a distinct managerial and strategic logic in the age of agentic AI. While most firms still operate within human-driven or AI-augmented models, we argue that we are now entering a phase where agentic AI (systems capable of initiating, coordinating, and adapting actions autonomously) can increasingly execute the core mechanisms of value creation, delivery, and capture. This shift reframes AI not as a tool to support strategy, but as the strategy itself. Using two illustrative cases, getswan.ai, an Israeli startup pursuing autonomy by design, and a hypothetical reconfiguration of Ryanair as an AI-driven incumbent, we depict the evolution from augmented to autonomous business models. We show how ABMs reshape competitive advantage through agentic execution, continuous adaptation, and the gradual offloading of human decision-making. This transition introduces new forms of competition between AI-led firms, which we term synthetic competition, where strategic interactions occur at rapid, machine-level speed and scale. It also challenges foundational assumptions in strategy, organizational design, and governance. By positioning agentic AI as the central actor in business model execution, the article invites us to rethink strategic management in an era where firms increasingly run themselves.
Zero-Shot Cognitive Impairment Detection from Speech Using AudioLLM
Venue: arXiv cs.SD 2025
Year: 2025

Cognitive impairment (CI) is of growing public health concern, and early detection is vital for effective intervention. Speech has gained attention as a non-invasive and easily collectible biomarker for assessing cognitive decline. Traditional CI detection methods typically rely on supervised models trained on acoustic and linguistic features extracted from speech, which often require manual annotation and may not generalise well across datasets and languages. In this work, we propose the first zero-shot speech-based CI detection method using the Qwen2- Audio AudioLLM, a model capable of processing both audio and text inputs. By designing prompt-based instructions, we guide the model in classifying speech samples as indicative of normal cognition or cognitive impairment. We evaluate our approach on two datasets: one in English and another multilingual, spanning different cognitive assessment tasks. Our results show that the zero-shot AudioLLM approach achieves performance comparable to supervised methods and exhibits promising generalizability and consistency across languages, tasks, and datasets.
PasteTrace: A Single Source Plagiarism Detection Tool For Introductory Programming Courses
Venue: arXiv cs.CY 2025
Year: 2025

Introductory Computer Science classes are important for laying the foundation for advanced programming courses. However, students without prior programming experience may find these courses challenging, leading to difficulties in understanding concepts and engaging in academic dishonesty such as plagiarism. While there exists plagiarism detection techniques and tools, not all of them are suitable for academic settings, especially in introductory programming courses. This paper introduces PasteTrace, a novel open-source plagiarism detection tool designed specifically for introductory programming courses. Unlike traditional methods, PasteTrace operates within an Integrated Development Environment that tracks the student's coding activities in real-time for evidence of plagiarism. Our evaluation of PasteTrace in two introductory programming courses demonstrates the tool's ability to provide insights into student behavior and detect various forms of plagiarism, outperforming an existing well-established tool. A video demonstration of PasteTrace and its source code, and case study data are made available at https://doi.org/10.6084/m9.figshare.27115852
Secret Sharing in 5G-MEC: Applicability for joint Security and Dependability
Venue: arXiv cs.CR 2025
Year: 2025

Multi-access Edge Computing (MEC), an enhancement of 5G, processes data closer to its generation point, reducing latency and network load. However, the distributed and edge-based nature of 5G-MEC presents privacy and security challenges, including data exposure risks. Ensuring efficient manipulation and security of sensitive data at the edge is crucial. To address these challenges, we investigate the usage of threshold secret sharing in 5G-MEC storage, an approach that enhances both security and dependability. A (k,n) threshold secret sharing scheme splits and stores sensitive data among n nodes, requiring at least k nodes for reconstruction. The solution ensures confidentiality by protecting data against fewer than k colluding nodes and enhances availability by tolerating up to n-k failing nodes. This approach mitigates threats such as unauthorized access and node failures, whether accidental or intentional. We further discuss a method for selecting the convenient MEHs to store the shares, considering the MEHs' trustworthiness level as a main criterion. Although we define our proposal in the context of secret-shared data storage, it can be seen as an independent, standalone selection process for 5G-MEC trustworthy node selection in other scenarios too.
Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study
Venue: arXiv cs.CL 2025
Year: 2025

Tutoring improves student achievement, but identifying and studying what tutoring actions are most associated with student learning at scale based on audio transcriptions is an open research problem. This present study investigates the feasibility and scalability of using generative AI to identify and evaluate specific tutor moves in real-life math tutoring. We analyze 50 randomly selected transcripts of college-student remote tutors assisting middle school students in mathematics. Using GPT-4, GPT-4o, GPT-4-turbo, Gemini-1.5-pro, and LearnLM, we assess tutors' application of two tutor skills: delivering effective praise and responding to student math errors. All models reliably detected relevant situations, for example, tutors providing praise to students (94-98% accuracy) and a student making a math error (82-88% accuracy) and effectively evaluated the tutors' adherence to tutoring best practices, aligning closely with human judgments (83-89% and 73-77%, respectively). We propose a cost-effective prompting strategy and discuss practical implications for using large language models to support scalable assessment in authentic settings. This work further contributes LLM prompts to support reproducibility and research in AI-supported learning.
Beyond the Link: Assessing LLMs' ability to Classify Political Content across Global Media
Venue: arXiv cs.CL 2025
Year: 2025

The use of large language models (LLMs) is becoming common in political science and digital media research. While LLMs have demonstrated ability in labelling tasks, their effectiveness to classify Political Content (PC) from URLs remains underexplored. This article evaluates whether LLMs can accurately distinguish PC from non-PC using both the text and the URLs of news articles across five countries (France, Germany, Spain, the UK, and the US) and their different languages. Using cutting-edge models, we benchmark their performance against human-coded data to assess whether URL-level analysis can approximate full-text analysis. Our findings show that URLs embed relevant information and can serve as a scalable, cost-effective alternative to discern PC. However, we also uncover systematic biases: LLMs seem to overclassify centrist news as political, leading to false positives that may distort further analyses. We conclude by outlining methodological recommendations on the use of LLMs in political science research.
Open Sky, Open Threats: Replay Attacks in Space Launch and Re-entry Phases
Venue: arXiv cs.CR 2025
Year: 2025

This paper examines the effects of replay attacks on the integrity of both uplink and downlink communications during critical phases of spacecraft communication. By combining software-defined radios (SDRs) with a real-time channel emulator, we replicate realistic attack conditions on the Orion spacecraft's communication systems in both launch and reentry. Our evaluation shows that, under replay attacks, the attacker's signal can overpower legitimate transmissions, leading to a Signal to Noise Ratio (SNR) difference of up to -7.8 dB during reentry and -6.5 dB during launch. To mitigate these threats, we propose a more secure receiver design incorporating a phase-coherency-dependent decision-directed (DD) equalizer with a narrowed phase-locked loop (PLL) bandwidth. This configuration enhances resilience by making synchronization more sensitive to phase distortions caused by replay interference.
OmniReflect: Discovering Transferable Constitutions for LLM agents via Neuro-Symbolic Reflections
Venue: arXiv cs.AI 2025
Year: 2025

Efforts to improve Large Language Model (LLM) agent performance on complex tasks have largely focused on fine-tuning and iterative self-correction. However, these approaches often lack generalizable mechanisms for longterm learning and remain inefficient in dynamic environments. We introduce OmniReflect, a hierarchical, reflection-driven framework that constructs a constitution, a compact set of guiding principles distilled from task experiences, to enhance the effectiveness and efficiency of an LLM agent. OmniReflect operates in two modes: Self-sustaining, where a single agent periodically curates its own reflections during task execution, and Co-operative, where a Meta-advisor derives a constitution from a small calibration set to guide another agent. To construct these constitutional principles, we employ Neural, Symbolic, and NeuroSymbolic techniques, offering a balance between contextual adaptability and computational efficiency. Empirical results averaged across models show major improvements in task success, with absolute gains of +10.3% on ALFWorld, +23.8% on BabyAI, and +8.3% on PDDL in the Self-sustaining mode. Similar gains are seen in the Co-operative mode, where a lightweight Qwen3-4B ReAct agent outperforms all Reflexion baselines on BabyAI. These findings highlight the robustness and effectiveness of OmniReflect across environments and backbones.
BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing
Venue: arXiv cs.CV 2025
Year: 2025

We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compositing pipeline: (i) segmenting and converting visual inputs into editable 3D entities (layering), (ii) editing them in Blender with 3D-grounded control (editing), and (iii) fusing them into a coherent scene using a generative compositor (compositing). Our generative compositor extends a pre-trained diffusion model to process both the original (source) and edited (target) scenes in parallel. It is fine-tuned on video frames with two key training strategies: (i) source masking, enabling flexible modifications like background replacement; (ii) simulated object jittering, facilitating disentangled control over objects and camera. BlenderFusion significantly outperforms prior methods in complex compositional scene editing tasks.
Automata on $S$-adic words
Venue: arXiv cs.FL 2025
Year: 2025

A fundamental question in logic and verification is the following: for which unary predicates $P_1, \ldots, P_k$ is the monadic second-order theory of $\langle \mathbb{N}; <, P_1, \ldots, P_k \rangle$ decidable? Equivalently, for which infinite words $α$ can we decide whether a given Büchi automaton $A$ accepts $α$? Carton and Thomas showed decidability in case $α$ is a fixed point of a letter-to-word substitution $σ$, i.e., $σ(α) = α$. However, abundantly more words, e.g., Sturmian words, are characterised by a broader notion of self-similarity that uses a set $S$ of substitutions. A word $α$ is said to be directed by a sequence $s = (σ_n)_{n \in \mathbb{N}}$ over $S$ if there is a sequence of words $(α_n)_{n \in \mathbb{N}}$ such that $α_0 = α$ and $α_n = σ_n(α_{n+1})$ for all $n$; such $α$ is called $S$-adic. We study the automaton acceptance problem for such words and prove, among others, the following. Given finite $S$ and an automaton $A$, we can compute an automaton $B$ that accepts $s \in S^ω$ if and only if $s$ directs a word $α$ accepted by $A$. Thus we can algorithmically answer questions of the form "Which $S$-adic words are accepted by a given automaton $A$?"
Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and engaging with this emerging technology through three research directions. First, I demonstrate how the institutional adoption of AI detectors introduces systematic biases, particularly disadvantaging writers of non-dominant language varieties, highlighting critical equity concerns in AI governance. Second, I present novel population-level algorithmic approaches that measure the increasing adoption of LLMs across writing domains, revealing consistent patterns of AI-assisted content in academic peer reviews, scientific publications, consumer complaints, corporate communications, job postings, and international organization press releases. Finally, I investigate LLMs' capability to provide feedback on research manuscripts through a large-scale empirical analysis, offering insights into their potential to support researchers who face barriers in accessing timely manuscript feedback, particularly early-career researchers and those from under-resourced settings.